unrpa

模拟Photoshop中的磁力套索的算法，主要是先用Opencv做梯度相关处理，然后用Djisktra最短路径搜索找到边缘。(Magnetic Lasso implementation using Python)

说明：  读取文件夹下的图片，缩放分辨率，存为tensorflow数据集tf格式，以制备数据集，方便之后直接读取，同时提供了tf数据集转普通img和numpy格式(Read the picture under the folder, zoom the resolution, save it as tensorflow data set TF format, to prepare data set, easy to read directly after.)

DBSCAN算法的重点是选取的聚合半径参数和聚合所需指定的MinPts数目。  在此使用球面距离来衡量地理位置的距离，来作为聚合的半径参数。  如下实验，选取2公里作为密度聚合的半径参数，MinPts个数为5.

A commented bash script to generate our final 2nd place solution can be found in make_kaggle_solution.sh. Running all the commands sequentially will probably take 7 - 10 days on recent consumer grade hardware. If you have multiple GPUs you can speed things up by doing training and feature extraction for the two networks in parallel. However, due to the computationally heavy data augmentation it may be far less than twice as fast especially when working with 512x512 pixel input images. You can also obtain a quadratic weighted kappa score of 0.839 on the private leaderboard by just training the 4x4 kernel networks and by performing only 20 feature extraction iterations with the weights that gave you the best MSE validation scores during training. The entire ensemble only achieves a slightly higher score of 0.845.

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说明：  Nussinov算法，预测rna二级结构(This program implements Nussinov algorithm on predicting RNA structure from RNA sequence using dynamic programming approach. The baisic idea is similiar with Smith-Waterman algorithm, obtaining optimal result about predicting RNA secondary structure. This program is able to deal with "bulge","hairpin loop" efficiently but can not handle "pseudoknot . )

python核心编程3,适合有一定python基础的朋友，这本书讲了gui编程，网络编程，多线程，正则以及Django框架等各方面知识，比较全面。(core python programming(3rd))

详细讲解了宽度学习系统的具体推导以及其实现过程。(A detail paper of BLS.)

金融时间序列分析 1. 采用Pandas从Yahoo网上下载上市公司的5到10年的日收盘数据，上证指数的日收盘数据。 2. 计算上市公司和上证指数的收益率， 3. 针对上市公司收益率进行ARMA建模，确定P和q，并对残差进行分析，最后向前预测多期，显示预测图。 4. 针对上市公司收益率进行ARCH建模，确定阶数，并对残差进行分析，最后进行预测。 5. 针对上市公司收益率进行GARCH建模，确定阶数，并对残差进行分析，最后进行预测。(use Arch Model to ananlyse and predict yields from public companies. Python. pandas)

WGS84坐标系下坐标转换,大地坐标与空间直角坐标相互转换